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Market penetration analyses involves establishing a relation between proprietary corporate data and population and economic data at a spatial level. A company´s strengths and weaknesses based on objective measures thereby become measurable and visible at a small scale. The presentation occurs on digital maps. The corporate data are first geocoded and in most cases aggregated as well. The typical presentation forms are topical maps and density analyses.



Market penetration analyses give companies answers to questions such as:
  • Where do my customers reside?
  • Where are my customers from, and what does the regional distribution look like?
  • Where do I make a high turnover, where low turnover, and which areas are terra incognita?
  • What interconnections exist in the high-turnover areas?
  • To what extent has my customer and sales potential already been exploited?
  • Where do I need to react with targeted advertising?
  • Where was the most recent advertising campaign successful?
  • Did the advertising campaign really select the right ad locations?
  • How many people were not reached in the new customer campaign?
  • Where do the target audiences with the highest purchasing power reside?
  • Can sales slumps be traced to competitors´ locations?

Density analysis


Density analysis converts point data to n-dimensional representation.

Both local technical data (e.g., customer -> turnover or competition -> sales area) and their spatial positions are thereby correlated. In addition, the spatial relation and the corresponding technical data value are translated to a colour scale and thus represent the topical-spatial weighting clearly.



Advantage:
Most people are better able to perceive and understand surfaces than a series of points.

Spatial patterns resulting from the spatial distribution of points thus quickly become clear and are more easily visually comprehensible to the viewer.

Modern GIS and geomarketing systems make use of this technique and help to make complex spatial and topical technical data more quickly comprehensible.

Distance matrices


A distance matrix depicts the spatial distance between m and n points. Either linear distance (simple case) or the actual distance due to calculation in street networks may underlie the ascertained distances.

Distance matrices play a significant role in expansion models, accessibilities or mobility behaviour.

Various data sources can be used as a starting point for calculating distances. Calculation between customer data (e.g., from store cards) and POS locations is classic. Should no personal data be present, aggregated and anonymised data from mobile telephone networks (radio cells entered, length of stay in the radio cell) can be used as a starting or end point of a distance matrix, for example. This method is obvious for estimating the expected customer flow at an existing or planned shopping centre, for instance.



Advantage:
Distance matrices are a significant source of information
  • in the area of operative location planning
  • sales force planning and
  • sales region planning
Distance matrices are also used in handling service cases.